HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset
This provides a resource for researchers and practitioners in recommender systems to address data sparsity and improve models in the hotel domain, though it is incremental as it focuses on dataset creation.
The authors tackled the lack of large-scale datasets for hotel recommendations by introducing HotelRec, a dataset based on TripAdvisor with 50 million reviews, which is significantly larger than existing datasets (e.g., 50M vs. 0.9M).
Today, recommender systems are an inevitable part of everyone's daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models require a large number of data to achieve their best performance. Many datasets fulfilling this criterion have been proposed for multiple domains, such as Amazon products, restaurants, or beers. However, works and datasets in the hotel domain are limited: the largest hotel review dataset is below the million samples. Additionally, the hotel domain suffers from a higher data sparsity than traditional recommendation datasets and therefore, traditional collaborative-filtering approaches cannot be applied to such data. In this paper, we propose HotelRec, a very large-scale hotel recommendation dataset, based on TripAdvisor, containing 50 million reviews. To the best of our knowledge, HotelRec is the largest publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). We release HotelRec for further research: https://github.com/Diego999/HotelRec.